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While it is true that an unedited photograph may be an index, digital technology is eroding the viewer's confidence that the image is an objective representation of reality. Further, the photographer made conscious decisions about the composition of the image, how to light it, whether to take a close-up or long shot, etc.
According to Roland Barthes the coded iconic message is the story that the image portrays. This message is easily understood and the images represent a clear relationship. [1] The "reader" of the image applies their knowledge to the encoding of the photo. An image of a bowl of fruit for example might imply still life, freshness or market stalls ...
An image conditioned on the prompt an astronaut riding a horse, by Hiroshige, generated by Stable Diffusion 3.5, a large-scale text-to-image model first released in 2022. A text-to-image model is a machine learning model which takes an input natural language description and produces an image matching that description.
Flux (also known as FLUX.1) is a text-to-image model developed by Black Forest Labs, based in Freiburg im Breisgau, Germany. Black Forest Labs were founded by former employees of Stability AI. As with other text-to-image models, Flux generates images from natural language descriptions, called prompts.
In semiotics, signified and signifier (French: signifié and signifiant) are the two main components of a sign, where signified is what the sign represents or refers to, known as the "plane of content", and signifier which is the "plane of expression" or the observable aspects of the sign itself.
Ideogram was founded in 2022 by Mohammad Norouzi, William Chan, Chitwan Saharia, and Jonathan Ho to develop a better text-to-image model. [3]It was first released with its 0.1 model on August 22, 2023, [4] after receiving $16.5 million in seed funding, which itself was led by Andreessen Horowitz and Index Ventures.
An example of prompt usage for text-to-image generation, using Fooocus. Prompts for some text-to-image models can also include images and keywords and configurable parameters, such as artistic style, which is often used via keyphrases like "in the style of [name of an artist]" in the prompt [91] and/or selection of a broad aesthetic/art style.
General scheme of content-based image retrieval. Content-based image retrieval, also known as query by image content and content-based visual information retrieval (CBVIR), is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases (see this survey [1] for a scientific overview of the CBIR field).